Quantitatively, we compare our approach with CosyPose [1]}, a state-of-the-art multi-view pose fusion solution which takes the object pose estimates from individual viewpoints as the input and optimizes the overall scene consistency. Note that, CosyPose is an offline batch-based solution that is agnostic to any particular pose estimator. For a fair comparison, we use the same pose estimator (LINE-2D template matching with the same bounding boxes, object center, and segmentation mask from the object center localization network). To feed the reliable single-view estimates to CosyPose, we use two strategies to obtain the scale information for the LINE-2D pose estimator. For the first strategy, we generate the templates at multiple distances in the training time (9 in our experiments) and perform the standard template matching in run-time. This strategy can significantly improve the single view pose estimation performance by sacrificing the run-time speed and is treated as the RGB version of CosyPose in our experiments. For the second strategy, we directly use the depth images at run-time to acquire the object scale and refer to it as the RGBD version for CosyPose. Note that, for our approach, we only use RGB images without any depth data.
